The U.S. healthcare industry is a $2T economy with the rate of growth far exceeding that of general inflation. With the aging global population, the current healthcare crisis is expected to worsen, threatening the health of global economy. The existing healthcare ecosystem is zero-sum. The recent pay-for-performance (P4P) experiment by the National Health Services in the United Kingdom resulted in mixed outcomes with incentive-based payments far exceeding the budget with uncertain improvements in patient health. On the other hand, a recent study on the sophistication of healthcare consumers reveals that there is little correlation between consumers' perception of care and the actual quality of healthcare delivered as measured by RAND's 236 quality indicators. Furthermore, given the high chum rate and the propensity of employers to seek the lowest-cost health plan, payers are motivated to focus primarily on reducing short-term cost and carving out the cream-of-the-crop population, resulting in perverse benefit design.
In healthcare, predictive models are used to improve underwriting accuracies and to identify at-risk members for clinical programs, such as various condition-centric disease management programs. Unfortunately, predictive models typically use year-1 payer claims data to predict year-2 cost. Some predictive modeling vendors predict future inpatient or emergency-room episodes since they represent high-cost events. The emphasis on cost makes sense given that the impetus for predictive models came from private and government payers struggling with rising healthcare costs.
Evidence-based medicine (EBM) is an attempt to apply scientific evidence to making care decisions for patients. A lot of EBM guidelines are derived from medical journals, where teams of researchers rely on randomized controlled trials and observational studies to draw inferences on the efficacy of various treatments on carefully selected patient populations. Pharmacovigilance or study of adverse drug reactions is an example of EBM.
Current EBM vendors, such as Active Health Management, a wholly owned subsidiary of Aetna, and Resolution Health, rely on a team of physicians reading and codifying relevant medical journals. The resulting EBM database is applied to population claims data consisting of medical, Rx, and lab claims data in order to identify patients not receiving proper EBM guidelines, i.e., with “gaps” in treatment. Physicians of the identified patients are contacted through faxes or telephone calls with instructions or recommendations on how to close the gaps in treatment. A number of shortcomings exist with the current EBM implementation. Many EBM studies suffer from small sample size, thus making generalization difficult and sometimes inaccurate. A corollary of the first shortcoming is that most EBM studies are at a selected population level and do not provide drilldown information at a sub-population level. That is, if not everyone benefits from an EBM guideline, it may be dangerous to apply the guideline to the entire study population, which begs for a careful tradeoff between specificity and sensitivity. Guidelines typically do a poor job of translating study outcomes into metrics that end stakeholders care about. For example, payers pay a particular attention to cost, which is not the same as improving surrogate endpoints that are therapeutic in nature with various time frames for healing or outcomes improvement. Publication bias and conflicting results encourage ad hoc decision making on the part of payers in the area of utilization management, such as coverage denials and medical necessity reviews. Furthermore, relying on published guidelines discourages the use of autonomous or loosely guided search for anomalies or precursors to adverse outcomes using a large of amount of integrated data assets and intelligent search algorithms based on machine learning.
Clearly, there is a desperate need for an integrated solution for providing healthcare management.